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As a successful solution applied to electrical discharge machining (EDM), powder-mixed electrical discharge machining (PMEDM) has been proposed as an upgrade of the EDM process. The optimization of the process parameters of PMEDM is essential and pressing. In this study, Taguchi methods and analysis of variance (ANOVA) were used to find the main parameters affecting surface roughness in the EDM process with SiC powder-mixed-dielectric of hardened 90CrSi steel. The PMEDM parameters selected were the powder concentration, the pulse-on-time, the pulse-off-time, the pulse current, and the server voltage. It was found that SiC powder exhibits positive effects on reducing surface roughness. The roughness obtained with the optimum powder concentration of 4 g/L was reduced by 30.02% compared to that when processed by conventional EDM. Furthermore, the pulse-off-time was found to be the most influential factor that gave an important effect on surface roughness followed by the powder concentration. The EDM condition including a powder concentration of 4 g/L, a pulse-on-time of 6 µs, a pulse-off-time of 21 µs, a pulse current of 8 A, and a server voltage of 4 V resulted in the best surface roughness.

Cutting regime parameters play an important role in determining the efficiency of the grinding process and the quality of the ground parts. In this study, the influences of the cutting parameters, including the cutting depth (ae), the feed rate (Fe) and the wheel speed (RPM) on the grinding time when grinding tablet shape punches by a cubic boron nitride (CBN) wheel on a CNC (Computerized Numerical Control) milling machine are investigated. The Taguchi technique based on orthogonal array and analysis of variance (ANOVA) was then applied to design the number of experiments and evaluate the influence of cutting depth, feed rate and wheel speed on the grinding time. The results show that among the three cutting parameters, the most influential parameter on the grinding time is the cutting depth. The second influential parameter on the grinding time is the feed rate. The least influential parameter on grinding time is the wheel speed. In addition, the optimal condition of cutting parameters obtained for grinding tablet shape punches by cubic boron nitride wheels on a CNC milling machine are a cutting depth of 0.03 mm, wheel speed of 5000 rpm and feed rate of 3500 mm/min. This optimum cutting parameters ensure the least grinding time.

This paper presents a new approach to developing the torque model in deep hole drilling, both for conventional and ultrasonic assisted drilling processes. The model was proposed as a sum of three components: the cutting, the chip evacuation and the stick-slip torques. Parameters of the new model were carried out by applying the regression analysis technique, with the correlation values higher than 0.999. The data were collected from 36 experimental dry drilling tests, both in conventional and ultrasonic assisted cutting conditions, with the depth-to-diameter of the drilled holes of 7.5. The major advantage of the new model compared to previous models is that the new model of chip-evacuation torque has only one coefficient, thus making it easier to evaluate and compare different deep-drilling processes. The effectiveness of ultrasonic assistance in deep hole drilling was also highlighted using the proposed model. The new model is promising to predict critical depth and torque in deep hole drilling.

This study is aimed at determining optimum partial gear ratios to minimize the cost of a three-stage helical gearbox. In this work, eleven input parameters were investigated to find their influence on the optimum gear ratios of the second and the third stages ( u 2 and u 3 ). To reach the goal, a simulation experiment was designed and implemented by a cost optimization program. The results revealed that in addition to the input parameters, their interactions also have important effects in which the total ratio gearbox ratio ( u t ) and the cost of shaft ( C s ) have the most impact on u 2 and u 3 responses, respectively. Moreover, the proposed models of the two responses are highly consistent to the experimental results. The proposed regression equations can be applied to solve optimization cost problems.